class_docstr: A model API that tags input sentence.
  
  Attributes:
      model: Model.
      preprocessor: Transformer. Preprocessing data for feature extraction.
      tokenizer: Tokenize input sentence. Default tokenizer is `str.split`.
docstr: Probability estimates.
  
  The returned estimates for all classes are ordered by the
  label of classes.
  
  Args:
      text : string, the input text.
  
  Returns:
      y : array-like, shape = [num_words, num_classes]
      Returns the probability of the word for each class in the model,
docstr_tok: ["Probability","estimates",".","The","returned","estimates","for","all","classes","are","ordered","by","the","label","of","classes",".","Args",":","text",":","string",",","the","input","text",".","Returns",":","y",":","array-like",",","shape","=","[","num_words",",","num_classes","]","Returns","the","probability","of","the","word","for","each","class","in","the","model",","]
code: def predict_proba(self, text):
      """Probability estimates.
  
      The returned estimates for all classes are ordered by the
      label of classes.
  
      Args:
          text : string, the input text.
  
      Returns:
          y : array-like, shape = [num_words, num_classes]
          Returns the probability of the word for each class in the model,
      """
      assert isinstance(text, str)
  
      words = self.tokenizer(text)
      X = self.preprocessor.transform([words])
      y = self.model.predict(X)
      y = y[0]  # reduce batch dimension.
  
      return y
  
file_path: anago/anago/tagger.py
imports: import numpy
  from seqeval.metrics.sequence_labeling import get_entities
label_desc: Use image node_id 2 for calling the Tagger obj's underlying member method code with example usage: obj.predict_proba(text) and returns: y